Reciprocity to Increase Participation of Compatible Living Donor and Recipient Pairs in Kidney Paired Donation
Bibliographic record
Abstract
Inclusion of compatible living donor and recipient pairs (CPs) in kidney paired donation (KPD) programs could increase living donor transplantation. We introduce the concept of a reciprocity-based strategy in which the recipient of a CP who participates in KPD receives priority for a repeat deceased donor transplant in the event their primary living donor KPD transplant fails, and then we review the practical and ethical considerations of this strategy. The strategy limits prioritization to CPs already committed to living donation, minimizing the risk of unduly influencing donor behavior. The provision of a tangible benefit independent of the CP's actual KPD match avoids many of the practical and ethical challenges with strategies that rely on finding the CP recipient a better-matched kidney that might provide the CP recipient a future benefit to increase KPD participation. Specifically, the strategy avoids the potential to misrepresent the degree of future benefit of a better-matched kidney to the CP recipient and minimizes delays in transplantation related to finding a better-matched kidney. Preliminary estimates suggest the strategy has significant potential to increase the number of living donor transplants. Further evaluation of the acceptance of this strategy by CPs and by waitlisted patients is warranted.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".